CDP Deployment, part 1: Getting off to the right start
This is the ultimate, unbiased, vendor agnostic check-list distilled to the must-have aspects for CDP deployment success. Learn how mParticle can help you implement a CDP correctly the first time around.
IN THIS ISSUE: SUCCESSFUL DEPLOYMENT - PART 1
- Best practices for customer data platform (CDP) deployment: distilled down to the must-haves for deployment success
- Key milestones in the deployment journey: know when and what to celebrate
- Deployment timelines: 6 weeks? 12 weeks? 24 weeks?
- Possible deployment models: various approaches and which ones might work best for you
SECTION 1: THE DEFINITIVE DEPLOYMENT BEST PRACTICES CHECKLIST
We spoke to multiple experts in the CDP space and based on all the responses, put together a checklist of deployment best practices that can help set you up for the greatest success. We consolidated recurring themes and points, as well as highlighted some useful contributions in the quotes peppering this section. This is the ultimate, unbiased, vendor agnostic check-list distilled to the must-have aspects for CDP deployment success.
Read the full article here.
Latest from mParticle
Get your flywheel in motion with Data Master
Learn how mParticle's Data Master enables you to increase data quality throughout the customer data pipeline, allowing insights to compound, and making every campaign and product launch better than the last.
GOAT: Lifecycle marketing for scalable growth
Learn how GOAT uses mParticle to streamline their data pipeline and increase Customer Lifetime Value.
mParticle launches new features to help brands create ‘data flywheel’
New features for seamless data quality management, and transformation to serve as a foundation for improved customer experience and better insights.
Better data, better insights, better results: Helping brands create a data flywheel
Introduce total quality management and enforcement into your customer data pipeline with new Data Master features and Calculated Attributes. With Data Master and Calculated Attributes, establishing a source of reliable customer data that will create a customer data flywheel, where the data quality and data’s impact on the product cycle will continuously improve over time.